Friday, February 24, 2006

The second edition of the good folks at the Hardball Times annual is much-improved from their first effort. Not that it was bad, but most of the articles were reprinted from what had appeared on their site, and the stat section seemed to be the main focus. This year, the stat section is back with all of the data it had previously, plus stuff (such as Win Shares for example) that did not appear last year.

There are reviews of all six divisional races as well as the playoffs. The playoff reviews incorporate WE graphs, which is a very nice touch, although I don’t completely agree with the games they selected for this treatment (but I should note that all World Series games are shown). This is followed by a commentary section on the 05 season, of which I especially liked the articles on the business side of the game and the World Baseball Classic.

The next section includes essays on baseball history and is highlighted by Bill James’ guest appearance to analyze Bert Blyleven’s win-loss records (there is also a guest piece by Rob Neyer in the book). As with anything else, the essay sections include some that have topics that interest me and some that don’t; some that I like and some that I think are overly pretentious. But on the whole, they are well worth reading.

The next section it entitled “Analysis” and is the kind of sabermetric stuff that is probably right up the alley of people who read this blog. The first is an article by John Dewan on the significance of the 100 pitch level. He finds that pitchers who average >100 pitches in the first half of the season perform better in the second half then pitchers who average <100 pitches in the first half but put up similar ERAs. I have a few problems with this study. The first is that pitchers who average more pitches have probably pitched more innings, and therefore given us more evidence that they are quality pitchers then others who make less pitches. Dewan’s data may suggest this as the gap in second half performance is less noticeable for the groups with high first half ERAs. I don’t want to overstate this point though, as pitches probably have a loose correlation with innings, since pitches are determined both by the amount of work and the style of the pitcher.

But the second problem I have is that managers are more likely to allow better pitchers to make >100 pitches then poorer pitchers. If you have Randy Johnson and Aaron Small on your staff, you do not start the season with a clean slate on your opinion of their ability. If Johnson is roughed up in the early innings, you still feel he is a quality pitcher, and you are likely to have a slower hook with him. It is the same problem that makes pitch counts, batters faced, etc. so hard to study--the best pitchers pitch more, just because they are the best pitches.

The second article is Dan Fox’s look at “luck” which I discussed previously here. Then Studes checks in with a piece that shows the empirical LW values of each event as well as for batted ball type. The only thing I would question here is that he uses the overall values for each event (S,D,T,etc.) times the percentage of line drives(or GB, etc.) that result in that event. It is possible that when a certain type of ball results in a hit, the run value is higher and lower. Perhaps more infield flies become singles when the infield is in for instance, and have a higher run value. I don’t know this to be true, and I don’t believe the effect would be big at all, and I’m not saying it calls into question the data presented at all--just that it would be interesting to see if certain types of events on a given type of batted ball occurred more or less often.

Studes then looks at parks, including a look at park factors for batted ball type. This article is very interesting, and I don’t say that just because he mentions the park factors I publish annually on my website. The questionable thing here is the reference to yours truly as “an internet baseball wonk”. Don’t get me wrong, I am not offended, but I have never been described as a “wonk” before. I have always associated the term “wonk” with a specific type of nerd or geek or what have you--one who studies the details of political stuff (i.e. “policy wonk”). I suppose that the word has broader applications, and that the implication I just mentioned is simply the most common. Regardless, being a “wonk” is much more flattering then some other terms that could be applied.

Studes goes for a trifecta with an article looking at DER and a runs above average approach that considers batted ball type. The encouraging thing to me about this article is that the DER actually seems to track the more accurate measure fairly well, so it is still presumably a decent gauge of fielding performance for periods before the more advanced data is available.

JC Bradbury of Sabernomics fame and David Gassko investigate the correlations of various event and batted ball rates for individuals from year-to-year, and in a related article Bradbury uses batted ball data to estimate OPS. Dan Fox then reviews his baserunning analysis methodology (Incremental Runs), and Studes finishes the section with a look at player contracts using Win Shares. To me, the analysis section was definitely the highlight of the book and contained a lot of interesting and though-provoking stuff.

The stats section includes a lot of the basic stats, plus RC, Win Shares, Incremental Runs, and some more basic data that you can’t find anywhere else, like errors broken down into fielding and throwing, double plays started, and percentages of PAs resulting in various batted ball types.

All-in-all, the folks at the Hardball Times have vastly improved their book from a year ago. And last year’s edition is not one that I regret purchasing. So I can definitely recommend this edition.

The only thing I would question here is that he uses the overall values for each event (S,D,T,etc.) times the percentage of line drives(or GB, etc.) that result in that event. It is possible that when a certain type of ball results in a hit, the run value is higher and lower.

Interesting. I also doubt that the effect is big, but it's worth thinking about.

it would be interesting to see if certain types of events on a given type of batted ball occurred more or less often.

I don't think that's the way to analyze it. Obviously there are more extra base hits on flyballs, more singles on grounders, etc. The question is whether a line-drive double is worth more than a flyball line-drive (or vice versa).

We obviously can't use our favorite method, which is looking at how many Base Runs or Runs Created you would add/subtract by adding each type of method. But I think we can use the two next-best methods.

One way is to use regression. But instead of just regressing runs against singles, doubles, outs, etc., you'd separate the outcomes by batted-ball types. So you'd regress runs against groundball singles, line-drive singles, flyball singles, etc.

The other way would be to use play-by-play data. Look at the expected run value before and after all flyball doubles and then do the same for all line-drive doubles. And so on for all outcome/batted-ball types.

Or, if you want to cut right to the chase and find the average value of a line drive, forget splitting line drives into outs, singles, doubles, triples, homers, etc. Instead, just use either of the two methods I listed on batted-ball type, regardless of outcome. For example, regress runs against grounders, liners, OF flies, IF flies, K and BB.

Like I said at the top of the post, I don't expect the differences to be huge, but there may be a couple cases where they are, and those would be interesting.

Vinay wrote:I don't think that's the way to analyze it. Obviously there are more extra base hits on flyballs, more singles on grounders, etc. The question is whether a line-drive double is worth more than a flyball line-drive (or vice versa).

Yeah, that was sloppily worded on my part. I agree with your last sentence.

I had the empirical PBP approach in mind when I was writing that. Barring that, I think the second regression approach you mentioned would be the way to go, as the first seems as if it would be unwieldly because it has so many variables that all have various correlations with each other.

And I agree that the differences would probably be small if they exist at all, and would not in any way invalidate the analysis that Studes did in the piece.